MAMMOGRAPHIC IMAGING BY DIFFUSION TOMOGRAPHY

描述（改编自申请人的摘要）：本发明的长期目标是： 研究开发一种近红外时间分辨光学成像 系统基于在频域中执行的测量， 检测乳腺组织中的肿瘤。 由于强烈的散射， 组织的光，从光学产生高质量图像的能力， 测量将需要评估信号， 分散的 本研究的具体目标是识别和优化 通过解剖学评价进行数据收集和分析的条件 女性乳房的精确光学（AAO）模型。 申请人已经 我们之所以采用这种方法，是因为我们认为它是一种有效的， 用于评估和优化关键成像的成本效益高的方法 参数的条件下，非常类似的那些将是 从实际测量中获得，无需众多的工程和其他 实际的限制和明显更高的成本， 在涉及人类受试者的类似研究中会遇到。 的 申请人将通过指定光学吸收和 散射系数到相应的组织结构中识别， 健康志愿者的女性乳房的分段3-D MRI图像， 已知有肿瘤和非肿瘤病变的患者。 测定 将使用先前描述的 双积分球测量技术在新制备的薄 从手术标本中获得的乳房组织切片。 明确 将通过显微镜检查进行组织类型分配 染色的相邻组织切片 最佳条件的确定 将基于对数据收集所需权衡的审查， 解决方案的准确性，和所需的计算时间，并将进行两个 方向 前向问题的解决方案将涉及计算 各种AAO的探测器响应和相关成像算子 模型介质作为目标几何形状的函数， 大小和对比度不同的病理性内含物。 这些将涉及 评估新的可用的，高效的代码，以解决 运输和扩散方程，从而允许直接回答 扩散解决方案的适用性的重要问题 方程，这是计算效率比前者，根据 与解剖结构、几何结构和光学结构非常相似的条件 女性乳房的特性。 图像反问题 重建将通过求解线性扰动方程来评估 对于选定的数据集，使用各种方法， 计算效率和处理问题的能力， 病态和病态 先前开发的剥层 和多重网格计划将得到评估和进一步改进;这将 包括开发小波变换方法。 的敏感性和 重建图像对数据和成像算子中噪声的准确性， 照明方案、视角和源探测器数量的选择 将探讨立场和其他参数。

DESCRIPTION (Adapted from Applicant's Abstract): The long term goal of this research is to develop a near infrared (NIR) time- resolved optical imaging system based on measurements performed in the frequency domain for the detection of tumors in breast tissue. Because of the intense scattering of light by tissue, the ability to generate high quality images from optical measurements will require evaluation of signals that have been highly scattered. The specific goal of this study is to identify and optimize conditions for data collection and analysis by evaluation of anatomically accurate optical (AAO) models of the female breast. The applicants have adopted this approach because we believe it represents an efficient and cost-effective means for evaluation and optimization of critical imaging parameters under conditions that closely resemble those that would be obtained from actual measurement, without the numerous engineering and other practical constraints and significantly higher costs that almost certainly would be encountered in similar studies involving human subjects. The applicants will construct AAO models by assigning optical absorption and scattering coefficients to the corresponding tissue structures identified in segmented 3-D MRI images of the female breast of healthy volunteers and patients having known neoplastic and non-neoplastic lesions. Determination of these coefficients will be performed using a previously described dual-integrating sphere measurement technique on freshly prepared thin slices of breast tissue obtained from surgical specimens. Definitive assignment of the tissue type will be performed by microscopic examination of stained adjacent tissue sections. Determination of optimal conditions will be based on examination of the tradeoffs required for data collection, solution accuracy, and required computing time, and will proceed in two directions. Solutions to the forward problem will involve computing detector responses and associated imaging operators for the various AAO model media as a function of target geometry and presence of simulated pathological inclusions of varying size and contrast. These will involve evaluation of newly available, highly efficient codes for solution of the transport and diffusion equations, thereby permitting a direct answer to the important question of the suitability of solutions to the diffusion equation, which is computationally more efficient than the former, under conditions that closely resemble the anatomy, geometry and optical properties of the female breast. The inverse problem for image reconstruction will be evaluated by solving a linear perturbation equation for selected data sets, using a variety of approaches that vary in their computational efficiency and ability to deal with problems stemming from ill-conditioning and ill-posedness. Previously developed layer-stripping and multigrid schemes will be evaluated and further improved; this will include development of a wavelet transform method. The sensitivity and accuracy of reconstructed images to noise in the data and imaging operators, choice of illumination scheme, view angle, and number of source-detector positions and other parameters will be explored.